Chronic low back pain (CLBP) is the most common chronic pain condition and is the second leading cause of disability in the U.S. Pain catastrophizing (PC)--a pattern of negative cognitive-emotional responses to actual or anticipated pain-is significantly associated with the development and maintenance of CLBP as well as disability. PC undermines CLBP treatments, thus contributing to a cycle of treatment futility and wasted expenditure. While Cognitive Behavioral Therapy (pain-CBT) treats PC, group treatment involves 6-10 sessions and thus poses substantial burdens of time, travel, and cost. Therefore, there is a critical need to develop and disseminate efficient, low-cost treatments that specifically reduce PC. Accordingly, we developed a single-session, 2-hour pain-CBT class that solely treats PC (From Catastrophizing to Recovery; FCR). Our pilot data revealed large effect sizes for FCR in a chronic pain sample and superior outcomes for medical and psychological across PROMIS domains, as compared to a 'treatment as usual' matched clinic cohort. We propose to conduct a 3-arm comparative efficacy RCT in 231 patients with CLBP comparing: (A) FCR, (B) a health education control, and (C) an 8-session pain-CBT class. Our primary endpoint is PC 3 months post- treatment and our secondary endpoint is PC 6 months post-treatment. We hypothesize that FCR will be superior to active control and non-inferior to the 8-session pain-CBT class for improving PC and pain-related outcomes measured by our PROMIS platform. An innovative aspect of the application is our proposal to develop and validate a brief version Daily PCS measure, and apply the measure with high frequency sampling methods to elucidate the mechanics of PC, and to characterize how positive response to active intervention reduces the influence of PC episodes. Additional novel methods our specialized PROMIS platform; actigraphy for objective sleep and activity measurement; and a customized 'FCR Relaxation Resource' app (on Nexus 7 tablets) to objectively quantify skills use in the FCR group. Our rich dataset will allow for detailed phenotyping of responders / non-responders for both active treatments using machine learning and other advanced analytics. We will use daily ratings across a longitudinal timeframe to characterize how PC changes in response to treatment, as well as the mechanistic influence of PC on pain, sleep, activity, and other variables. Our proposal addresses the NCCAM priorities to (1) alleviate chronic pain, (2) study our mind-body intervention in a real-world setting, and (3) advance scientific understanding of the mechanisms of PC.